Case Focus: Detailed Analysis of High-Risk Countries and Situations

In this section, we focus on specific high-risk countries and situations to provide more detailed insights into the security challenges faced by aid workers in these environments. By examining these cases in depth, we can identify context-specific patterns and risk factors that may inform more targeted security measures.

1. High-Risk Country Profiles

First, let’s identify the most dangerous countries for aid workers based on our analysis of incident frequency and severity.

import plotly.graph_objects as go

country_incidents = df.groupby('Country').size().reset_index(name='Incident Count')
country_deaths = df.groupby('Country')['Total killed'].sum().reset_index(name='Deaths')
country_injuries = df.groupby('Country')['Total wounded'].sum().reset_index(name='Injuries')
country_kidnappings = df.groupby('Country')['Total kidnapped'].sum().reset_index(name='Kidnappings')
country_risk = country_incidents.merge(country_deaths, on='Country')
country_risk = country_risk.merge(country_injuries, on='Country')
country_risk = country_risk.merge(country_kidnappings, on='Country')
country_risk['Total Casualties'] = country_risk['Deaths'] + country_risk['Injuries'] + country_risk['Kidnappings']
country_risk = country_risk[country_risk['Incident Count'] > 5]
top_risk_countries = country_risk.sort_values('Incident Count', ascending=False).head(5)
fig = go.Figure()

fig.add_trace(go.Bar(
    x=top_risk_countries['Country'],
    y=top_risk_countries['Incident Count'],
    name='Incident Count',
    marker_color='#636EFA'
))

fig.add_trace(go.Bar(
    x=top_risk_countries['Country'],
    y=top_risk_countries['Deaths'],
    name='Deaths',
    marker_color='#d62728'
))

fig.add_trace(go.Bar(
    x=top_risk_countries['Country'],
    y=top_risk_countries['Injuries'],
    name='Injuries',
    marker_color='#ff7f0e'
))

fig.add_trace(go.Bar(
    x=top_risk_countries['Country'],
    y=top_risk_countries['Kidnappings'],
    name='Kidnappings',
    marker_color='#b58b00'
))
fig.update_layout(
    title={
        'text': 'Top 5 High-Risk Countries for Aid Workers',
        'y':0.97,
        'x':0.5,
        'xanchor': 'center',
        'yanchor': 'top'
    },
    xaxis_title='Country',
    yaxis_title='Count',
    barmode='group',
    height=500,
    width=800,
    margin=dict(l=80, r=150, t=100, b=80),
    legend=dict(orientation="v", yanchor="top", y=1, xanchor="right", x=1.1), 
    template='plotly_white'
)
fig.show()

This chart reveals the top five countries with the highest number of security incidents affecting aid workers. Afghanistan clearly stands out as the most dangerous country, with the highest number of incidents and a relatively balanced distribution of deaths, injuries, and kidnappings. South Sudan follows as the second most dangerous country, with a notably higher proportion of injuries compared to deaths and kidnappings, suggesting different attack patterns or medical response capabilities in this region.

Now, let’s visualize the relative proportions of different types of casualties in these high-risk countries:

import plotly.graph_objects as go
fig = go.Figure()

for i, country in enumerate(top_risk_countries['Country']):
    country_data = top_risk_countries[top_risk_countries['Country'] == country].iloc[0]
    total = country_data['Total Casualties']
    if total > 0:  # Avoid division by zero
        death_pct = country_data['Deaths'] / total * 100
        injury_pct = country_data['Injuries'] / total * 100
        kidnap_pct = country_data['Kidnappings'] / total * 100
        
        fig.add_trace(go.Pie(
            values=[death_pct, injury_pct, kidnap_pct],
            labels=['Deaths', 'Injuries', 'Kidnappings'],
            name=country,
            domain=dict(
                x=[i/5, (i+1)/5],  # Position each pie chart horizontally
                y=[0, 1]
            ),
            hole=.4,
            title=dict(
                text=country,
                position="bottom center"
            ),
            marker=dict(colors=['#d62728', '#ff7f0e', '#b58b00'])
        ))
fig.update_layout(
    title={
        'text': 'Distribution of Casualty Types in Top 5 Risk Countries',
        'y':0.97,
        'x':0.5,
        'xanchor': 'center',
        'yanchor': 'top'
    },
    height=400,
    width=800,
    showlegend=True,
    legend=dict(orientation="h", yanchor="bottom", y=-0.3, xanchor="center", x=0.5),  # Adjusted legend position
    margin=dict(l=80, r=80, t=100, b=100),  # Increased bottom margin for legend
    template='plotly_white'
)
fig.show()

These pie charts provide a more detailed view of the proportional distribution of casualties across the top risk countries. The visualization confirms that Afghanistan has a relatively balanced distribution of casualty types. South Sudan shows a predominance of injuries.

2. Risk Score Comparison Using Radar Charts

Let’s create a radar chart to compare different risk dimensions across the high-risk countries:

import plotly.graph_objects as go
radar_data = top_risk_countries.copy()
for country in radar_data['Country']:
    country_df = radar_data[radar_data['Country'] == country]
    
    if country_df['Incident Count'].values[0] > 0:
        radar_data.loc[radar_data['Country'] == country, 'Avg Casualties per Incident'] = (
            country_df['Total Casualties'] / country_df['Incident Count']
        ).values[0]
    else:
        radar_data.loc[radar_data['Country'] == country, 'Avg Casualties per Incident'] = 0
        
    if country_df['Total Casualties'].values[0] > 0:
        radar_data.loc[radar_data['Country'] == country, 'Death Rate (%)'] = (
            country_df['Deaths'] / country_df['Total Casualties'] * 100
        ).values[0]
    else:
        radar_data.loc[radar_data['Country'] == country, 'Death Rate (%)'] = 0

metrics = ['Incident Count', 'Deaths', 'Injuries', 'Kidnappings',
          'Avg Casualties per Incident', 'Death Rate (%)']

fig = go.Figure()
for i, country in enumerate(radar_data['Country']):
    country_values = []
    
    for metric in metrics:
        value = radar_data[radar_data['Country'] == country][metric].values[0]
        country_values.append(value)
    
    fig.add_trace(go.Scatterpolar(
        r=country_values,
        theta=metrics,
        fill='toself',
        name=country
    ))

fig.update_layout(
    title={
        'text': 'Security Risk Dimensions by Country',
        'y':0.97,
        'x':0.5,
        'xanchor': 'center',
        'yanchor': 'top'
    },
    polar=dict(
        radialaxis=dict(
            visible=True,
            range=[0, max([radar_data[metric].max() for metric in metrics]) * 1.1]
        )
    ),
    height=600,
    width=800,
    showlegend=True,
    margin=dict(l=80, r=80, t=100, b=80),
    template="plotly_white"
)
fig.show()

The non-normalized radar chart shows the absolute values across different risk dimensions, but due to varying scales across metrics, some dimensions dominate the visualization. Afghanistan clearly occupies the largest area, indicating its high overall risk profile across multiple dimensions. However, this chart makes it difficult to compare relative risks across countries for dimensions with smaller numerical values.

Let’s create a normalized version of the radar chart for better comparison:

import plotly.graph_objects as go
normalized_data = radar_data.copy()
for metric in metrics:
    max_val = normalized_data[metric].max()
    if max_val > 0:  # Avoid division by zero
        normalized_data[metric + '_norm'] = normalized_data[metric] / max_val * 10
    else:
        normalized_data[metric + '_norm'] = 0
fig = go.Figure()
for i, country in enumerate(normalized_data['Country']):
    norm_values = []
    
    for metric in metrics:
        value = normalized_data[normalized_data['Country'] == country][metric + '_norm'].values[0]
        norm_values.append(value)
    
    fig.add_trace(go.Scatterpolar(
        r=norm_values,
        theta=metrics,
        fill='toself',
        name=country
    ))

fig.update_layout(
    title={
        'text': 'Normalized Risk Profile Comparison (Scale: 0-10)',
        'y':0.97,
        'x':0.5,
        'xanchor': 'center',
        'yanchor': 'top'
    },
    polar=dict(
        radialaxis=dict(
            visible=True,
            range=[0, 10]
        )
    ),
    height=600,
    width=800,
    showlegend=True,
    margin=dict(l=80, r=80, t=100, b=80),
    template="plotly_white"
)
fig.show()

The normalized radar chart allows for a more meaningful comparison of risk profiles across countries. Afghanistan still occupies a significant area, indicating high risk across multiple dimensions, but interestingly its death rate is not the highest among these countries. South Sudan shows a particularly pronounced spike in injuries, confirming our earlier observation.

3. Attack Characteristics in High-Risk Countries

Let’s analyze the most common attack methods in these high-risk countries:

import plotly.express as px
top_countries = top_risk_countries['Country'].tolist()
high_risk_data = df[df['Country'].isin(top_countries)]
attack_methods = []
countries = []
counts = []
for country in top_countries:
    country_data = high_risk_data[high_risk_data['Country'] == country]
    
    method_counts = country_data['Means of attack'].value_counts()
    
    top_methods = method_counts.head(5)
    
    for method, count in top_methods.items():
        if not pd.isna(method) and method != 'Unknown':
            attack_methods.append(method)
            countries.append(country)
            counts.append(count)

attack_data = pd.DataFrame({
    'Country': countries,
    'Attack Method': attack_methods,
    'Count': counts
})

fig = px.bar(
    attack_data,
    x='Country',
    y='Count',
    color='Attack Method',
    barmode='stack'
)

fig.update_layout(
    title={
        'text': 'Most Common Attack Methods in High-Risk Countries',
        'y':0.97,
        'x':0.5,
        'xanchor': 'center',
        'yanchor': 'top'
    },
    height=500,
    width=800,
    xaxis_title='Country',
    yaxis_title='Number of Incidents',
    legend_title='Attack Method',
    margin=dict(l=80, r=80, t=100, b=80),
    template='plotly_white'
)

fig.show()

This chart reveals distinctive attack method patterns across the high-risk countries. Afghanistan shows a predominance of shootings and kidnappings, consistent with common insurgent tactics in the region. South Sudan’s incidents mainly involve shootings and bodily assault, which aligns with our earlier finding of higher injury rates and lower fatality rates in this country, as bodily assault typically results in fewer fatalities than other attack methods.

A particularly striking feature is the significant proportion of aerial bombardment incidents in Syria, reflecting the nature of the conflict there, which has involved extensive air campaigns by various actors. This finding explains why Syria might have different casualty patterns compared to countries where ground-based attacks predominate.

Let’s examine the distribution of incidents by year for these high-risk countries:

import plotly.express as px

country_year_incidents = high_risk_data.groupby(['Country', 'Year']).size().reset_index(name='Incidents')

fig = px.line(
    country_year_incidents,
    x='Year',
    y='Incidents',
    color='Country',
    markers=True
)

fig.update_layout(
    title={
        'text': 'Annual Incident Trends in High-Risk Countries',
        'y':0.97,
        'x':0.5,
        'xanchor': 'center',
        'yanchor': 'top'
    },
    height=500,
    width=800,
    xaxis_title='Year',
    yaxis_title='Number of Incidents',
    legend_title='Country',
    hovermode='x unified',
    margin=dict(l=80, r=80, t=100, b=80),
    template='plotly_white'
)

fig.show()

The temporal analysis reveals significant year-to-year variations in incident frequency across countries. Afghanistan experienced an extraordinary peak in incidents in 2013, which may correspond to a period of increased insurgent activity or reduced security measures. Sudan shows a notable surge in 2024, which is concerning and suggests a deteriorating security situation that requires immediate attention. Each country exhibits its own temporal pattern, reflecting the distinct conflict dynamics and security conditions in each context.

4. Seasonal Patterns Analysis

Now, let’s visualize the monthly patterns in these high-risk countries:

import plotly.express as px
country_month_incidents = high_risk_data.groupby(['Country', 'Month']).size().reset_index(name='Incidents')
fig = px.line(
    country_month_incidents,
    x='Month',
    y='Incidents',
    color='Country',
    markers=True
)
month_names = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
fig.update_xaxes(
    tickvals=list(range(1, 13)),
    ticktext=month_names
)

fig.update_layout(
    title={
        'text': 'Monthly Incident Patterns in High-Risk Countries',
        'y':0.97,
        'x':0.5,
        'xanchor': 'center',
        'yanchor': 'top'
    },
    height=500,
    width=800,
    xaxis_title='Month',
    yaxis_title='Number of Incidents',
    legend_title='Country',
    hovermode='x unified',
    margin=dict(l=80, r=80, t=100, b=80),
    template='plotly_white'
)

fig.show()

The monthly patterns reveal interesting seasonal variations in security incidents. Each country shows a distinct monthly distribution, with no universal seasonal trend across all countries. Afghanistan experiences a concentration of incidents during summer months, which could be related to the “fighting season” when weather conditions are more conducive to military operations. Syria shows fewer incidents in spring and autumn but more in winter and summer, possibly reflecting seasonal military campaign patterns. These seasonal variations have important implications for operational planning and risk management in humanitarian operations.

Let’s visualize the monthly patterns in a different way using a heatmap:

import plotly.graph_objects as go
import numpy as np
fig = go.Figure()

for i, country in enumerate(top_countries):
    country_data = high_risk_data[high_risk_data['Country'] == country]
    
    month_counts = np.zeros(12)
    for _, row in country_data.iterrows():
        month = row['Month']
        if not pd.isna(month):
            month_idx = int(month) - 1  
            if 0 <= month_idx < 12:  
                month_counts[month_idx] += 1
    
    visible = (i == 0)
    
    fig.add_trace(
        go.Heatmap(
            z=[month_counts],
            x=month_names,
            y=[country],
            colorscale='Reds',
            visible=visible,
            showscale=(i==0)
        )
    )

buttons = []
for i, country in enumerate(top_countries):
    visibility = [j == i for j in range(len(top_countries))]
    buttons.append(
        dict(
            label=country,
            method="update",
            args=[{"visible": visibility,
                   "showscale": visibility},
                  {"title": {"text": f"Monthly Incident Pattern in {country}",
                             "y": 0.97,
                             "x": 0.5,
                             "xanchor": "center",
                             "yanchor": "top"}}]
        )
    )

fig.update_layout(
    updatemenus=[
        dict(
            active=0,
            buttons=buttons,
            direction="down",
            pad={"r": 10, "t": 10},
            showactive=True,
            x=0.1,
            xanchor="left",
            y=1.2,
            yanchor="top"
        )
    ]
)

fig.update_layout(
    title={
        'text': f"Monthly Incident Pattern in {top_countries[0]}",
        'y':0.97,
        'x':0.5,
        'xanchor': 'center',
        'yanchor': 'top'
    },
    height=400,
    width=800,
    xaxis_title="Month",
    yaxis_title="Country",
    margin=dict(l=80, r=80, t=120, b=80),
    template="plotly_white"
)

fig.show()

This interactive heatmap provides a clearer visualization of the monthly incident patterns for each country, reinforcing our observations about seasonal variations. The dropdown menu allows for easy comparison between countries, highlighting the distinct monthly patterns in each context. These visualizations underscore the importance of considering seasonal factors in security planning for humanitarian operations.

5. Comparative Outcome Analysis

Now let’s compare the types of outcomes (deaths, injuries, kidnappings) for these high-risk countries using a scatter plot:

import plotly.express as px

fig = px.scatter(
    top_risk_countries,
    x='Deaths',
    y='Injuries',
    size='Kidnappings',
    color='Country',
    hover_name='Country',
    size_max=60
)

fig.update_layout(
    title={
        'text': 'Relationship Between Different Types of Casualties',
        'y':0.97,
        'x':0.5,
        'xanchor': 'center',
        'yanchor': 'top'
    },
    height=600,
    width=800,
    xaxis_title='Number of Deaths',
    yaxis_title='Number of Injuries',
    legend_title='Country',
    margin=dict(l=80, r=80, t=100, b=80),
    template='plotly_white'
)

fig.show()

This bubble chart provides a powerful visualization of how different types of casualties relate to each other across high-risk countries. Afghanistan stands out dramatically, occupying an extreme position in both deaths and injuries, with a large bubble size indicating numerous kidnappings as well. Sudan shows a distinctive pattern with a particularly high number of injuries relative to deaths. This chart effectively highlights the varying casualty profiles across countries, reflecting differences in conflict dynamics, attack methods, and potentially medical response capabilities.

Summary of High-Risk Country Analysis

Our detailed examination of high-risk countries has revealed crucial patterns and insights that can inform targeted security strategies for humanitarian operations:

1. Distinct Risk Profiles:
   • Afghanistan emerges as the highest-risk environment overall, with the most incidents and a balanced distribution of deaths, injuries, and kidnappings
   • South Sudan shows a distinctive pattern with injuries significantly outnumbering deaths and kidnappings, reflecting its particular conflict dynamics
   • Syria stands out for its high proportion of aerial bombardment incidents, a unique characteristic among the examined countries
   • Each country presents a different risk signature requiring tailored security approaches
2. Temporal and Seasonal Variations:
   • Significant year-to-year fluctuations exist, with Afghanistan experiencing a major peak in 2013 and Sudan showing a concerning surge in 2024
   • Monthly patterns vary dramatically by country: Afghanistan incidents concentrate in summer months, while Syria shows a bimodal distribution with peaks in winter and summer
   • These temporal patterns have critical implications for the timing of humanitarian operations and security planning
3. Attack Method Variations:
   • Attack methods show strong country-specific patterns: Afghanistan sees predominantly shootings and kidnappings, South Sudan features shootings and bodily assault, and Syria experiences unique aerial bombardment threats
   • These differences explain the varying casualty profiles, with bodily assault in South Sudan leading to more injuries than deaths, while aerial bombardments in Syria result in different casualty patterns
4. Casualty Relationships:
   • The bubble chart reveals how deaths, injuries, and kidnappings relate differently across countries
   • Afghanistan occupies an extreme position across all dimensions, while Sudan shows a disproportionately high injury count
   • These patterns reflect not only differences in attack types but potentially also variations in medical response capabilities and reporting practices

These findings underscore the critical importance of context-specific security strategies. A one-size-fits-all approach to humanitarian security is clearly inadequate given the distinctive risk profiles, seasonal patterns, and attack characteristics in each country. Organizations must develop tailored security protocols that account for these specific risk factors, including seasonal planning, transport security measures that reflect local attack patterns, and medical preparedness aligned with the most likely casualty types in each context.

The surge in incidents in Sudan during 2024 merits particular attention and may require immediate security reassessment for operations in that region. Similarly, the consistent high-risk profile of Afghanistan calls for sustained and comprehensive security measures across all operational dimensions.